{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "initial_id",
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2023-12-08T05:58:45.288061600Z",
     "start_time": "2023-12-08T05:58:45.271052100Z"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "plt.rcParams['font.sans-serif']=['Simhei']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "outputs": [],
   "source": [
    "x = np.array(list(range(1, 11))).reshape(-1, 1) # x需要转换成n行，1列\n",
    "\n",
    "# reshape前，x=array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10])\n",
    "# reshape后，x=array([[ 1],[2],[3],[4],[5],[6],[7],[8],[9],[10]])"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-08T05:27:55.253386100Z",
     "start_time": "2023-12-08T05:27:55.246258300Z"
    }
   },
   "id": "236aaf09b80ea4c9"
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "outputs": [
    {
     "data": {
      "text/plain": "[5.56, 5.7, 5.91, 6.4, 6.8, 7.05, 8.9, 8.7, 9.0, 9.05]"
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = [5.56, 5.7, 5.91, 6.40, 6.80, 7.05, 8.90, 8.70, 9.00, 9.05]\n",
    "y"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-08T05:30:16.615780Z",
     "start_time": "2023-12-08T05:30:16.605762200Z"
    }
   },
   "id": "c87e32396bd36933"
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "outputs": [],
   "source": [
    "m1=DecisionTreeRegressor(max_depth=1)\n",
    "m2=DecisionTreeRegressor(max_depth=3)\n",
    "m3=LinearRegression()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-08T05:35:11.710151600Z",
     "start_time": "2023-12-08T05:35:11.691165900Z"
    }
   },
   "id": "36858477ee7fd2ae"
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "outputs": [
    {
     "data": {
      "text/plain": "DecisionTreeRegressor(max_depth=1)",
      "text/html": "<style>#sk-container-id-2 {color: black;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeRegressor(max_depth=1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeRegressor</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeRegressor(max_depth=1)</pre></div></div></div></div></div>"
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m1.fit(x,y)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-08T05:35:21.305213800Z",
     "start_time": "2023-12-08T05:35:21.289301800Z"
    }
   },
   "id": "8ba16f914167d572"
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "outputs": [
    {
     "data": {
      "text/plain": "DecisionTreeRegressor(max_depth=3)",
      "text/html": "<style>#sk-container-id-3 {color: black;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeRegressor(max_depth=3)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeRegressor</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeRegressor(max_depth=3)</pre></div></div></div></div></div>"
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m2.fit(x,y)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-08T05:35:26.586088500Z",
     "start_time": "2023-12-08T05:35:26.574298400Z"
    }
   },
   "id": "ee4ae4bf52b1275a"
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "outputs": [
    {
     "data": {
      "text/plain": "LinearRegression()",
      "text/html": "<style>#sk-container-id-4 {color: black;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m3.fit(x,y)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-08T05:35:34.639935Z",
     "start_time": "2023-12-08T05:35:34.618099100Z"
    }
   },
   "id": "c9fc08a3845fd7bc"
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "outputs": [],
   "source": [
    "# 生成测试集特征值\n",
    "testX = np.arange(0,10,0.01).reshape(-1,1)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-08T05:43:56.971021600Z",
     "start_time": "2023-12-08T05:43:56.957994200Z"
    }
   },
   "id": "272a2479d60c2a2a"
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "outputs": [],
   "source": [
    "yPre1=m1.predict(testX)\n",
    "yPre2=m2.predict(testX)\n",
    "yPre3=m3.predict(testX)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-08T05:43:58.977018700Z",
     "start_time": "2023-12-08T05:43:58.958841900Z"
    }
   },
   "id": "3796abadb42bbfc5"
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1600x900 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 结果可视化，查看区别\n",
    "plt.figure(figsize=(16,9),dpi=100)\n",
    "\n",
    "plt.scatter(x,y,label=\"原始数据\")\n",
    "\n",
    "plt.plot(testX,yPre1,label=\"决策树1\")\n",
    "plt.plot(testX,yPre2,label=\"决策树3\") # 过拟合了\n",
    "plt.plot(testX,yPre3,label=\"线性回归\")\n",
    "\n",
    "plt.xlabel(\"数据\")\n",
    "plt.ylabel(\"预测值\")\n",
    "plt.legend()\n",
    "\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-08T05:58:49.676217300Z",
     "start_time": "2023-12-08T05:58:49.374379Z"
    }
   },
   "id": "ae0ba633fec5b6d5"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false
   },
   "id": "268ec57ac2962918"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false
   },
   "id": "f218faa8f7da1064"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false
   },
   "id": "5b9893f083325514"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false
   },
   "id": "fb881d7cee71d215"
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
